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- Q91095810 description "article scientifique publié en 2018" @default.
- Q91095810 description "artículu científicu espublizáu n'agostu de 2018" @default.
- Q91095810 description "bài báo khoa học" @default.
- Q91095810 description "im August 2018 veröffentlichter wissenschaftlicher Artikel" @default.
- Q91095810 description "scientific article published on 22 August 2018" @default.
- Q91095810 description "wetenschappelijk artikel" @default.
- Q91095810 description "наукова стаття, опублікована в серпні 2018" @default.
- Q91095810 name "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 name "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 type Item @default.
- Q91095810 label "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 label "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 prefLabel "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 prefLabel "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 P1433 Q91095810-818370C4-0F82-4391-9F2A-F420ACE2B528 @default.
- Q91095810 P1476 Q91095810-7D50633B-0CCC-4C08-AFA4-841DF7C94AFD @default.
- Q91095810 P2093 Q91095810-14305066-81DE-488F-99D6-B5EFF1617659 @default.
- Q91095810 P2093 Q91095810-8CBBEF60-8991-4BC8-B96B-AC3FC64C4BB0 @default.
- Q91095810 P2093 Q91095810-A1908CB6-C1FF-4737-9CCE-01D9E1986DF4 @default.
- Q91095810 P2093 Q91095810-AF9B94BD-CEA0-4783-AE9F-EDDDA9ABBFAE @default.
- Q91095810 P304 Q91095810-1AF2BB6E-658B-4375-ADD8-8DF2FFF04B90 @default.
- Q91095810 P31 Q91095810-591E472C-79D7-448A-8650-5CFCE23F53BE @default.
- Q91095810 P356 Q91095810-DF4195E4-20E7-4ED2-AC2A-4851DEEFA1A4 @default.
- Q91095810 P433 Q91095810-91B06945-A680-4284-8315-6CA255B60042 @default.
- Q91095810 P478 Q91095810-46C7D7CC-CF55-427F-B1A4-F7F6D8C18A27 @default.
- Q91095810 P50 Q91095810-CA1589D0-0550-4742-8DCE-F196503DAA66 @default.
- Q91095810 P50 Q91095810-DD18B578-F093-44F9-B7C9-8514494A5E13 @default.
- Q91095810 P577 Q91095810-F7CA4ABF-B8AE-4BBA-956C-C2807FD5D2E7 @default.
- Q91095810 P698 Q91095810-431E2323-45D4-4B47-80E1-63B9D03BD115 @default.
- Q91095810 P921 Q91095810-AA0967EA-7B29-4F37-AD88-A64A17D99A2A @default.
- Q91095810 P356 IJU.13784 @default.
- Q91095810 P698 30136400 @default.
- Q91095810 P1433 Q15767019 @default.
- Q91095810 P1476 "Potential for computer-aided diagnosis using a convolutional neural network algorithm to diagnose fat-poor angiomyolipoma in enhanced computed tomography and T2-weighted magnetic resonance imaging" @default.
- Q91095810 P2093 "Junichiro Ishioka" @default.
- Q91095810 P2093 "Takahiko Soma" @default.
- Q91095810 P2093 "Yasuhisa Fujii" @default.
- Q91095810 P2093 "Yoh Matsuoka" @default.
- Q91095810 P304 "978-979" @default.
- Q91095810 P31 Q13442814 @default.
- Q91095810 P356 "10.1111/IJU.13784" @default.
- Q91095810 P433 "11" @default.
- Q91095810 P478 "25" @default.
- Q91095810 P50 Q88716498 @default.
- Q91095810 P50 Q89362070 @default.
- Q91095810 P577 "2018-08-22T00:00:00Z" @default.
- Q91095810 P698 "30136400" @default.
- Q91095810 P921 Q17084460 @default.